High resolution dot pattern

ABSTRACT

The present invention embraces a system for measuring the 3D shape of an object using a structured light projector and an image sensor such as a scanner or camera. The structured light projector “projects” a pseudo random dot pattern onto the object that is positioned on a planar surface. The image sensor captures the 3D image of the object from the reflective surface and determines the dimensions or shape of the object. The surface displays the projected dot pattern and defines a grid based on the projected dot pattern. The dot pattern comprising a plurality of dots distributed on the grid such that neighboring dots within a certain sub-window size are unique sub-patterns. The neighboring dots are arranged in a staggered grid format relative to one axis of grid.

CROSS-REFERENCE TO PRIORITY APPLICATION

The present application claims the benefit of U.S. Patent Application No. 62/257,322 for a Pattern for Object Dimensioning filed Nov. 19, 2015, which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to three dimensional (3D) sensing systems for providing non-contact 3D surface measurements. More particularly, the 3D sensing systems and method utilize structured light illumination and a dot pattern for facilitating the 3D surface measurements of an object.

BACKGROUND

Generally speaking, 3D sensing based on structured light projection of a pattern in conjunction with an imaging sensor may provide non-contact 3D surface measurements of an object or shape. In some embodiments the image sensor may be a camera. In other embodiments, the image sensor may be a scanner. Many publications on the subject have been produced describing a variety of patterns spatially and/or temporally coded, in order to achieve 3D scanning. Good motion tolerance may be relatively important for handheld 3D scanners so spatially coded patterns are typically used. The methods also need to accommodate the impact of the disparity between the camera and the light projection due to the parallax distance between the camera and light projector.

Commonly employed patterns are “pseudo random” dot patterns (such as that used by the Microsoft Kinect and others) or “column coded” patterns like the one described by Vuylsteke & Oosterlinck in 1990, which is based on a chessboard pattern with diagonal connection/disconnections to encode a column index. However, these patterns may lack the performance and accuracy desired in object measurements for industrial applications.

Therefore, a need exists to improve the quality of measurements of a 3D sensing system.

SUMMARY

Accordingly, in one aspect, the present invention embraces methods and systems to determine the dimensions or shape of an object using a 3D sensing system based on a structured light projection of a pseudo random dot pattern onto the object.

In an exemplary embodiment, a three dimensional (3D) sensing system comprises a light source for projecting a dot pattern with structured light illumination onto an object positioned on a surface; a grid that represents the projected dot pattern displayed on the surface. The dot pattern comprising a plurality of dots distributed on the grid such that neighboring dots within a certain sub-window size are a unique sub-pattern of dots. The neighboring dots within the certain sub-window size are arranged in a staggered grid format relative to one axis of grid.

The three dimensional (3D) sensing system further comprises an image sensor for capturing an image of the object reflected from the surface in order to identify a location of the dots in the captured images, and for determining dimensions or shape of the object based on 3D sensing of the object. The dots are located on the grid with subpixel accuracy based on a difference of Gaussian (DOG) algorithm plus bicubic interpolation.

Additionally, the dot pattern comprises a “pseudo random” dot pattern. The light source comprises a vertical-cavity surface-emitting laser (VCSEL). The object is positioned on a planar surface. A column index for possible combinations of the unique sub-pattern of dots is achieved by executing a De Bruijn sequence, followed by an encoding sequence. The neighboring dots in the staggered grid format are located in one of three possible positions on a vertical axis of the grid. The three possible positions are within 15% of a grid pitch of the grid in order to maintain row index determination.

In another exemplary embodiment, a method of illuminating an object for a three dimensional (3D) sensing system for determining dimensions of the object comprises transmitting a pseudo random dot pattern with structured light illumination onto the object located on a planar surface; distributing dots on a grid based on the pseudo random dot pattern such that a group of neighboring dots within a certain sub-window size is a unique sub-pattern of dots. The position of one or more dots in the unique sub-pattern of dots within the certain sub-window size is modified relative to one axis of the grid. Additionally, the method includes achieving a column index for possible combinations of the unique sub-pattern of dots by utilizing a De Bruijn sequence; capturing an image of the object; and determining the dimensions of the object based on 3D sensing of the object.

The modification of the position of the one or more dots relative to one axis of the grid comprises modifying the positions of the one or more dots on the one axis of the grid without impacting row index determination. The modification of the positions of one or more dots relative to one axis of the grid comprises modifying the positions of one or more dots relative to one axis of the grid by an amount of less than 15% of a grid pitch of the grid. The certain sub-window size for the unique sub-pattern comprises 10% to 15% of grid positions based on the pseudo random dot pattern. A number of possible positions for the dots on an axis vary depending on an image sensor resolution. The number of possible positions for the dots on a vertical axis may be three. The dots are located on the grid with subpixel accuracy based on a difference of Gaussian (DOG) algorithm plus bicubic interpolation.

In yet another exemplary embodiment, a pseudo random dot pattern for three dimensional (3D) sensing comprises a grid representing the pseudo random dot pattern; and a group of neighboring dots within a certain sub-window size of the grid that defines a unique sub-pattern of dots. The unique sub-pattern of dots comprises one or more dots whose positions are modified in one axis of the grid. The pseudo random dot pattern is projected onto an object with structured light illumination to obtain dimensional information of the object with 3D sensing. The unique sub-pattern of dots comprises a staggered grid of dots with three possible vertical positions for each dot on the grid. The three possible vertical positions for each dot in a 4×2 sub-window, which corresponds to four dots, encode a column index with 81 different values. The three possible vertical positions comprise shift up, shift down and no shift positions. The one or more dots whose positions are modified in one axis of the grid are modified by an amount less than 15% of a grid pitch of the grid in order to maintain row index determination. The dots are located on the grid with subpixel accuracy based on a difference of Gaussian (DOG) algorithm plus bicubic interpolation.

The foregoing illustrative summary, as well as other exemplary objectives and/or advantages of the invention, and the manner in which the same are accomplished, are further explained within the following detailed description and its accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a 3D sensing system for measuring objects based on structured light projection of a dot pattern.

FIG. 2 illustrates a high resolution dot pattern for a 3D sensing system.

FIG. 3 illustrates a dot pattern for a 3D sensing system where the dots are located with subpixel accuracy in a staggered grid format.

DETAILED DESCRIPTION

The present invention embraces a system for measuring the 3D shape of an object using a structured light projector and an image sensor such as a scanner or camera. The structured light projector “projects” a pseudo random dot pattern onto the object that is positioned on a surface. The image scanner captures the 3D image of the object that has reflected off a reflective surface and determines the dimensions of the object.

Although the physical world is three-dimensional, traditional cameras and imaging sensors are only be able to acquire two-dimensional (2D) images that lack the depth information. This fundamental restriction may greatly limit an ability to perceive and to understand the complexity of real-world objects. 3D sensing methods utilizing structured light illumination can facilitate 3D surface measurements of an object. Applications may include: determination of the shape of a package or product in a retail or industrial environment, precision shape measurement for production control (e.g., turbine blades), reverse engineering (e.g., obtaining precision CAD data from existing objects), volume measurement (e.g., combustion chamber volume in motors), classification of grinding materials and tools, precision structure measurement of ground surfaces, radius determination of cutting tool blades, and precision measurement of planarity.

As described above, systems for 3D object scanning may be based on pattern projection and may be referred to as a triangulation system. They comprise a structured light projector that emits a pattern and an image sensor that includes a recording system. The projected or emitted pattern reflects off a surface where the object is positioned and the reflected image is scanned by the recording system and the data is then processed so that a three-dimensional model of the scanned area may be constructed. A planar surface provides for optimal measurement results.

A function of the projected pattern is for extracting of information about spatial depth (the third dimension). The 3D information may be combined with a 3D point cloud that forms the basis for object and form recognition as well as volume measurement. A point cloud is a set of data points in some coordinate system. In a three-dimensional coordinate system, these points are usually defined by X, Y, and Z coordinates, and often may be intended to represent the external surface of an object. Point clouds may be created by 3D scanners.

More specifically, the term “3D imaging” refers to techniques that are able to acquire true 3D data, i.e., values of some property of a 3D object, such as the distribution of density, as a function the 3D coordinates x; y; z. Surface imaging deals with measurement of the x; y; z coordinates of points on the surface of an object. Since the surface is, in general, nonplanar, it is described in a 3D space, and the imaging problem is called 3D surface imaging. This process is also referred to as 3D surface measurement.

One principal method of 3D surface imaging is based on the use of “structured light”, i.e., active illumination of the object with specially designed 2D spatially varying intensity pattern. FIG. 1 illustrates embodiment 100 of a 3D sensing system for measuring objects based on structured light projection. As illustrated in FIG. 1, a spatially varying 2D structured illumination is generated by a special projector or a light source modulated by a spatial light modulator. Structured light projector 104 projects a pattern onto the surface where object 108 is located. In this case, the pattern is dot pattern 106. Image sensor 102 may be used to capture a 2D image of the object projected by structured light projector 104. Image sensor 102 may be a camera or scanner.

If the object is a planar surface without any 3D surface variation, the pattern shown in the acquired image may be similar to that of the projected structured light pattern. However, when the surface in the object is nonplanar, the geometric shape of the surface may distort the projected structured light pattern as seen from the camera. The principle of structured light 3D surface imaging techniques is to extract the 3D surface shape based on the information from the distortion of the projected structured light pattern. Accurate 3D surface profiles of objects in the object can be computed by using various structured light principles and algorithms.

One variant of pattern projection is a dot pattern, as previously noted. The projected pattern consists of grouped dots. The emitter or light source may be an infrared laser, so that the projected dot pattern may not be visible for the human eye. In this case, an infrared image sensor may be used for the pattern recognition. The image processing may compare the dot patterns with stored reference patterns. The distance between emitter and projection surface may be deduced from the distortions and detected changes in position. This variant of pattern projections may be used to scan moving objects.

With dot patterns, the light is concentrated into small dots, resulting in high energy per unit of surface area and achieves a good signal-to-noise ratio (SNR) in the captured images and a lower sensitivity to the ambient lighting compared to the chessboard approach where the light is distributed over 50% of the surface. Moreover, it is typically easier to detect and accurately locate isolated bright spots in a picture. A difference of Gaussian (DOG) algorithm plus bicubic interpolation can be employed to locate the spots with a subpixel accuracy.

FIG. 2 illustrates a dot pattern 200 for a 3D sensing system. Dot pattern 200 defines a grid that may be useful to analysis the information scanned from the dot pattern that reflects from the object and planar surface. The basic concept of two dimensional (2D) grid pattern techniques is to uniquely label every sub-window in the projected 2D pattern, such that the pattern in any sub-window is unique and fully identifiable with respect to its 2D position in the pattern. Dot pattern 200 may have a high resolution.

For 3D sensing, one typically identifies each dot within the captured images. To achieve this identification with a “pseudo-random” dot pattern, the dots are distributed on a grid so that the neighboring dots within a certain sub-window size constitute a unique sub-pattern, which results in a low dot density generally around 10% to 15% of the grid positions and consequently, a low number of 3D measurement points compared to the grid resolution.

On the other hand with a chessboard pattern and a column coding technique, one can achieve a number of 3D measuring points equal to the chessboard resolution, but the SNR is rather low in comparison to a dot pattern and may require much higher lighting power. Moreover, to produce such patterns, one generally uses a mask over the light source which absorbs about 50% of the light. This method may not the best approach for energy savings, which may be important, particularly for hand-held systems.

To achieve high resolution, fast and accurate image processing, good immunity to ambient illumination, and good power efficiency, the ideal pattern projection may be a fully populated regular grid of bright dots of light, as may be produced with VCSEL arrays. However in this case, it may not be possible to identify each dot within the captured images and therefore, one cannot perform the distance measurement for each dot, which is the basis for 3D reconstruction.

With a 3D scanning system based on structured light illumination, the disparity between dots is generally along one direction only (i.e., a horizontal disparity or a vertical disparity). For a horizontal disparity, within the captured images, each dot is shifted horizontally depending on the respective distance of each light dot to the object, and remains perfectly aligned horizontally. The vertical position of these rows of aligned dots is constant in the captured image, whatever the distance, which allows a determination of the row indexes.

With a dot pattern distributed on a regular grid, there may not be a method to determine the column indexes. With such a system, all the dots that belong to the same row have the same vertical position in the captured images. To add additional information, the presently disclosed pattern includes a slight modification to the vertical position of the dots on the grid source. For example, a basic approach suggests discriminating 3 different vertical positions. The number of vertical positions may be more (e.g., 5, 7, 10, 13, 15, 20, 30, 50, or any number of vertical positions) depending on the image sensor resolution and knowing that the dots can be located with a subpixel accuracy.

FIG. 3 illustrates dot pattern 300 with four dots in a 2×2 neighborhood (4×2 sub-window) with three possible vertical positions available for each dot. If the shift is small compared to the grid pitch (e.g., up to 15%) it may not impact the row index determination. With a 3 positions structure (no shift, shift up, shift down), each dot brings an additional 1.585 bit of information. The grid pitch is the distance between the centers of two adjacent dots, where the dots are symmetrically positioned on an imaginary grid.

If we consider the 2×2 neighborhood (4×2 sub-window), it means 6.34 bits (1.585 bits×4) of information is available to encode a column index with 81 different combinations. (The different combinations are equal to the number of positions for each dot, e.g., 3 positions, raised to the Nth power, where N is the number of dots, for example, 3⁴=81). To achieve a column index with all the possible combinations, the encoding sequence typically follows a De Bruijn sequence.

Per FIG. 3, dot pattern 300 represents the concept of a staggered grid (quinconce, quincunx, or in staggered rows) with three possible vertical positions for each dot (N, U, D or no shift, shift up, shift down) and using a 4×2 sub-window analysis, corresponding to 4 dots, in order to encode a column index with 81 different values.

The De Bruijn sequence used here is:

-   -   B(3,4)=NNNNUNNNDNNUUNNUDNNDUNNDDNUNUNDNUUUNUUDNUDUNUDDNDNDUUNDUDND         DUNDDDUUUUDUUDDUDUDDDD         and the corresponding sequence of the 81 column indexes:     -   0, 1, 3, 9, 27, 2, 6, 18, 55, 4, 12, 36, 28, 5, 15, 45, 56, 7,         21, 63, 29, 8, 24, 73, 57, 10, 30, 11, 33, 19, 58, 13, 39, 37,         31, 14, 42, 46, 59, 16, 48, 64, 32, 17, 51, 74, 60, 20, 61, 22,         66, 38, 34, 23, 69, 47, 62, 25, 75, 65, 35, 26, 79, 76, 67, 40,         41, 43, 49, 68, 44, 52, 77, 70, 50, 71, 53, 80, 78, 72, 54.

As previously noted, the presently disclosed pattern and method for achieving the pattern are merely exemplary. To this end, the forgoing concept and methods could be applied using different De Bruijn sequences, numbers of position shifts, horizontal, and/or vertical position shifts, shapes of dots, and/or neighborhood sizes to achieve different patterns falling within the scope of the presently disclosed concept.

Additional Exemplary Embodiments

In an exemplary embodiment, the pattern is a high resolution dot pattern that is projected into a field of view of an image sensor. In some exemplary embodiments, a vertical-cavity surface-emitting laser (VCSEL) emits the pattern.

In another exemplary embodiment, the present invention embraces a light source that projects a pattern for use in determining the dimensions of an object. In exemplary embodiments, the light source is a light emitting diode (LED). In exemplary embodiments, the light source is a VCSEL. The light source for emitting a pattern may be a high resolution dot pattern.

In yet another exemplary embodiment, the present invention embraces a dimensioning device that includes an image sensor having a field of view and a light source for projecting a dot pattern.

To supplement the present disclosure, this application incorporates entirely by reference the following commonly assigned patents, patent application publications, and patent applications:

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In the specification and/or figures, typical embodiments of the invention have been disclosed. The present invention is not limited to such exemplary embodiments. The use of the term “and/or” includes any and all combinations of one or more of the associated listed items. The figures are schematic representations and so are not necessarily drawn to scale. Unless otherwise noted, specific terms have been used in a generic and descriptive sense and not for purposes of limitation. 

The invention claimed is:
 1. A three dimensional (3D) sensing system, comprising: a light source for projecting a dot pattern with structured light illumination onto an object, the dot pattern comprising a plurality of dots distributed on a grid such that neighboring dots within a certain sub-window size are a unique sub-pattern of dots, wherein the neighboring dots within the certain sub-window size are arranged in a staggered grid format relative to one axis of grid and the dots are located on the grid with subpixel accuracy based on a difference of Gaussian (DOG) algorithm plus bicubic interpolation; and an image sensor for capturing an image of the object; wherein the system is configured to identify a location of the dots in the captured image and determine dimensions and/or a shape of the object.
 2. The system as in claim 1, wherein, the dot pattern comprises a “pseudo random” dot pattern.
 3. The system as in claim 1, wherein, the light source comprises a vertical-cavity surface-emitting laser (VCSEL).
 4. The system as in claim 1, wherein the object is positioned on a planar surface.
 5. The system as in claim 1, wherein, a column index for possible combinations of the unique sub-pattern of dots is achieved by executing a De Bruijn sequence, followed by an encoding sequence.
 6. The system as in claim 1, wherein, the neighboring dots in the staggered grid format are located in one of three possible positions on a vertical axis of the grid.
 7. The system as in claim 6, wherein, the three possible positions are within 15% of a grid pitch of the grid in order to maintain row index determination.
 8. A method of illuminating an object for a three dimensional (3D) sensing system for determining dimensions of the object, the method comprising: illumination an object with a pseudo random dot pattern, wherein the pseudo random dot pattern comprises a grid of dots such that a group of neighboring dots within a certain sub-window size is a unique sub-pattern of dots, wherein a position of one or more dots in the unique sub-pattern of dots within the certain sub-window size is modified relative to one axis of the grid, wherein the unique sub-pattern of dots has a column index based on a De Bruijn sequence; capturing an image of the object; and determining the dimensions of the object based on the captured object.
 9. The method of claim 8, wherein the modification of the position of the one or more dots relative to one axis of the grid comprises modifying the positions of the one or more dots on the one axis of the grid without impacting row index determination.
 10. The method of claim 9, wherein the modification of the positions of one or more dots relative to one axis of the grid comprises modifying the positions of one or more dots relative to one axis of the grid by an amount of less than 15% of a grid pitch of the grid.
 11. The method of claim 8, wherein the certain sub-window size for the unique sub-pattern comprises 10% to 15% of grid positions based on the pseudo random dot pattern.
 12. The method of claim 8, wherein a number of possible positions for the dots on an axis varies depending on an image sensor resolution.
 13. The method of claim 12, wherein the number of possible positions for the dots on a vertical axis is three.
 14. The method of claim 8, wherein the dots are located on the grid with subpixel accuracy based on a difference of Gaussian (DOG) algorithm plus bicubic interpolation.
 15. A pseudo random dot pattern light source for three dimensional (3D) sensing, comprising: a light source projecting a grid representing the pseudo random dot pattern and a group of neighboring dots within a certain sub-window size of the grid that defines a unique sub-pattern of dots, wherein the unique sub-pattern of dots comprises one or more dots whose positions are modified in one axis of the grid, and wherein the unique sub-pattern of dots comprises a staggered grid of dots with three possible positions for each dot on the grid.
 16. The pseudo random dot pattern of claim 15, wherein, the staggered grid of dots comprises three possible vertical positions for each dot on the grid.
 17. The pseudo random dot pattern of claim 16, wherein, the three possible vertical positions for each dot in a 4×2 sub-window, which corresponds to four dots, encodes a column index with 81 different values.
 18. The pseudo random dot pattern of claim 16, wherein, the three possible vertical positions comprise shift up, shift down and no shift positions.
 19. The pseudo random dot pattern of claim 16, wherein, the one or more dots whose positions are modified in one axis of the grid are modified by an amount less than 15% of a grid pitch of the grid in order to maintain row index determination.
 20. The pseudo random dot pattern of claim 15, wherein, the dots are located on the grid with subpixel accuracy based on a difference of Gaussian (DOG) algorithm plus bicubic interpolation. 